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New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

dc.contributor.authorde Oliveira Ferreira Silva, César
dc.contributor.authorMatulovic, Mariana [UNESP]
dc.contributor.authorLilla Manzione, Rodrigo [UNESP]
dc.contributor.institutionAgroicone
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2021-06-25T10:29:40Z
dc.date.available2021-06-25T10:29:40Z
dc.date.issued2021-06-01
dc.description.abstractAbstract: Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract: [Figure not available: see fulltext.]en
dc.description.affiliationAgroicone
dc.description.affiliationBiosystems Engineering Department School of Sciences and Engineering São Paulo State University (UNESP)
dc.description.affiliationUnespBiosystems Engineering Department School of Sciences and Engineering São Paulo State University (UNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2014/04524-7
dc.description.sponsorshipIdFAPESP: 2016/09737-4
dc.description.sponsorshipIdCNPq: 421782/2016-1
dc.identifierhttp://dx.doi.org/10.1007/s42452-021-04600-w
dc.identifier.citationSN Applied Sciences, v. 3, n. 6, 2021.
dc.identifier.doi10.1007/s42452-021-04600-w
dc.identifier.issn2523-3971
dc.identifier.scopus2-s2.0-85105228492
dc.identifier.urihttp://hdl.handle.net/11449/206293
dc.language.isoeng
dc.relation.ispartofSN Applied Sciences
dc.sourceScopus
dc.subjectArtificial intelligence in geosciences
dc.subjectComputer science in geosciences
dc.subjectData ethics
dc.titleNew dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater managementen
dc.typeArtigopt
dspace.entity.typePublication
unesp.author.orcid0000-0002-5152-6497[1]
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Ciências e Engenharia, Tupãpt

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